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MEMS gyro bias estimation in accelerated motions using sensor fusion of camera and angular-rate gyroscope

Nazemipour, A ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TVT.2020.2976975
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. Although the accuracy of MEMS gyroscopes has been extremely improved, in some aspects, such as stability of bias, they still suffer from some big error sources, like run-to-run bias, which determines the sensor price but is not negligible even inexpensive sensors. Due to the fact that run-to-run bias is a kind of stochastic parameter, it has to be measured by utilizing online methods. Utilizing a novel, fast and efficient vision-based rotation estimation algorithm for ground vehicles, we have developed a visual gyroscope that is used in our sensor fusion system, in order to estimate run-to-run bias of the MEMS gyroscope, accurately. Comparing with similar approaches that use GPS, odometer, accelerometer or magnetometer, the most important advantage of the proposed vision-based sensor-fusion framework is its accuracy in accelerated motions. Moreover, it can be used at environments that have a magnetic field, such as urban environments, without depending on external signals. We have evaluated the efficiency of the proposed system using real datasets, recorded from a car moving in urban areas. According to our experimental results, the proposed algorithm is capable of estimating bias of gyroscope after a convergence time of about 6 seconds and improving the accuracy of the MEMS gyroscope, which provides the possibility of using cheaper sensors for high-accuracy demands. © 1967-2012 IEEE
  6. Keywords:
  7. Camera ; Extended Kalman filter ; Gyroscope ; Run-to-run bias estimation ; Sensor fusion ; Visual gyroscope ; Ground vehicles ; Stochastic systems ; Accelerated motion ; Convergence time ; External signals ; Rotation estimations ; Sensor fusion systems ; Stochastic parameters ; Urban environments ; Vision-based sensors ; Gyroscopes ; MEMS gyroscopes
  8. Source: IEEE Transactions on Vehicular Technology ; Volume 69, Issue 4 , April , 2020 , Pages 3841-3851
  9. URL: https://ieeexplore.ieee.org/document/9018077